When executives hear the term "big data", they naturally think of an amazing amount of available data. This data comes from e-commerce and omni channel marketing, or from connected devices on the Internet of Things, or from applications that generate more detailed information about trading activities.
Big Data
When it comes to big data, many people can say some, but if you ask what are the core technologies of big data, it is estimated that many people will not be able to say
The British science and technology news media V3 recently listed 10 relevant misconceptions about big data applications.
To do big data, first of all, you should understand what is the core of your own enterprise or industry. We often find that many enterprises are defeated not by their current competitors, but by many competitors who are not your competitors. For a simple example, everyone thinks that Amazon is an e-commerce company, but this is wrong. Its main revenue now comes from the cloud (cloud service). That is to say, enterprises need to find their own core data (value).
What is the significance of digital transformation? This is the answer to the question that every enterprise is looking for, and the most common answer is the reinvention of business processes.
What we can predict is that the future of big data technology will continue to evolve along the direction of heterogeneous computing, cloudization, AI convergence, and in-memory computing.
Data visualization is mainly aimed at communicating and communicating information clearly and effectively with the help of graphical means.
Security solutions that work well locally or in the cloud can be vulnerable when used in a hybrid data center, and organizations need a new approach to meet the data security needs of hybrid data centers.
With the continuous improvement of big data infrastructure, data analytics and business intelligence tools will gradually become the mainstay of big data. Therefore, the big data industry will develop toward these trends in the coming years.
Companies tend to make their Big Data projects large in size and scope when implementing them, but the truth is that most Big Data projects usually end up in failure.